CGM - Assignment : Predicting Height of the Children 👧👦📏



Examples of various measurements are shown below:

img_1



NOTEBOOK Description 📗

NOTEBOOK Contents ✍️

1. Libraries Import

2. Dataset Import

Image Augmentations

3. How Image Augmentation provide us the better & bigger dataset❓

Performance Metrics

4. Defining Performance Metrics

Models Strategies

🧮 A. Fine_Tuning -- Last_3_Layers + Custom_TopLayers -- ImageNet Weights

🧮 B. Fine Tuning -- Custom TopLayers -- ImageNet Weights

🧮 C. DL FEATURES -- ML Regressor Model

🧮 D. TOP & BOTTOM Distance Features -- ML Regressor Model

SUMMARY

Import_Libraries

Dataset_Import

Data_Preparation

Reading the images
Image Data Generator

Image_Augmentation

OBSERVATION

Defining_Performance_Metrics

Models_Training_Configuration

Models

A.Fine_Tuning---Last_3_Layers__+__Custom_TopLayers---ImageNet_Weights

A1.ResNet---50

OBSERVATION

Plot the Loss and R2 Graphs

LOSS_Curves-1

OBSERVATION

R_Square_Curves-1

OBSERVATION

Evaluating the Model

VAL_Evaluation-1

OBSERVATION

TEST_Evaluation-1

OBSERVATION

Running TensorBoard - A1

model_1

OBSERVATIONS

B.Fine_Tuning---Custom_TopLayers---ImageNet_Weights

B1.ResNet---50

OBSERVATION

LOSS_Curves-2

OBSERVATION

R_Square_Curves-2

OBSERVATION

Evaluating the Model

VAL_Evaluation-2

OBSERVATION

TEST_Evaluation-2

OBSERVATION

Running TensorBoard - B1

model_2

OBSERVATIONS

Predictions_and_Scores_on_VAL_Set

OBSERVATION

C.DL_FEATURES---ML_Regressor_Model

Predictions_and_Scores_on_VAL_Set

OBSERVATION

D.TOP_and_BOTTOM_DistanceFeatures---ML_Regressor_Model

Predictions_and_Scores_on_TRAIN_Set

OBSERVATION

Predictions_and_Scores_on_VAL_Set

OBSERVATION

SUMMARY